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 "cells": [
  {
   "source": [
    "# Dive Into DL Pytorch"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "source": [
    "## Chapter 2 --- Prerequired knowledge\n",
    "\n",
    "### Section 2 --- AutoGrad"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[1., 1.],\n        [1., 1.]], requires_grad=True)\nNone\n"
     ]
    }
   ],
   "source": [
    "x = torch.ones(2, 2, requires_grad=True)\n",
    "print(x)\n",
    "print(x.grad_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[3., 3.],\n        [3., 3.]], grad_fn=<AddBackward0>)\n<AddBackward0 object at 0x7f8f364131d0>\n"
     ]
    }
   ],
   "source": [
    "y = x + 2\n",
    "print(y)\n",
    "print(y.grad_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[27., 27.],\n        [27., 27.]], grad_fn=<MulBackward0>) tensor(27., grad_fn=<MeanBackward0>)\n"
     ]
    }
   ],
   "source": [
    "z = y * y * 3\n",
    "out = z.mean()\n",
    "print(z, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "out.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[4.5000, 4.5000],\n        [4.5000, 4.5000]])\n"
     ]
    }
   ],
   "source": [
    "print(x.grad)"
   ]
  },
  {
   "source": [
    "## Chapter 3 --- Basics\n",
    "\n",
    "### Section 1 --- Linear Model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Using cpu device\n",
      "/home/yiwen/.anaconda3/envs/py37/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at  /opt/conda/conda-bld/pytorch_1623448265233/work/c10/cuda/CUDAFunctions.cpp:115.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from IPython import display\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Using {} device\".format(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([ 0.2435, -0.7149]) tensor(7.1030)\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "num_examples = 1000\n",
    "true_w = [2, -3.4]\n",
    "true_b = 4.2\n",
    "features = torch.tensor(np.random.normal(0, 1, (num_examples, num_inputs)), dtype=torch.float)\n",
    "labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b\n",
    "labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)\n",
    "\n",
    "print(features[0], labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "def use_svg_display():\n",
    "    # 用矢量图显示\n",
    "    display.set_matplotlib_formats('svg')\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    # 设置图的尺寸\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "\n",
    "set_figsize()\n",
    "plt.scatter(features[:, 1].numpy(), labels.numpy(), 1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.utils.data as Data\n",
    "\n",
    "batch_size = 10\n",
    "\n",
    "dataset = Data.TensorDataset(features, labels)\n",
    "\n",
    "data_iter = Data.DataLoader(dataset, batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from torch import optim\n",
    "\n",
    "class LinearNet(nn.Module):\n",
    "    def __init__(self, n_feature):\n",
    "        super(LinearNet, self).__init__()\n",
    "        self.linear = nn.Linear(n_feature, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        y = self.linear(x)\n",
    "        return y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "LinearNet(\n  (linear): Linear(in_features=2, out_features=1, bias=True)\n)\n"
     ]
    }
   ],
   "source": [
    "net = LinearNet(num_inputs).to(\"cuda\")\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "SGD (\nParameter Group 0\n    dampening: 0\n    lr: 0.01\n    momentum: 0\n    nesterov: False\n    weight_decay: 0\n)\n"
     ]
    }
   ],
   "source": [
    "loss = nn.MSELoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01)\n",
    "\n",
    "print(optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    size = len(dataloader.dataset)\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        # Compute prediction error\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y.view(-1,1))\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if batch % 10 == 0:\n",
    "            loss, current = loss.item(), batch * len(X)\n",
    "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(dataloader, model, loss_fn):\n",
    "    size = len(dataloader.dataset)\n",
    "    num_batches = len(dataloader)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred = model(X)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
    "    test_loss /= num_batches\n",
    "    correct /= size\n",
    "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "loss: 0.000132  [    0/ 1000]\nloss: 0.000119  [  100/ 1000]\nloss: 0.000160  [  200/ 1000]\nloss: 0.000113  [  300/ 1000]\nloss: 0.000222  [  400/ 1000]\nloss: 0.000067  [  500/ 1000]\nloss: 0.000055  [  600/ 1000]\nloss: 0.000239  [  700/ 1000]\nloss: 0.000079  [  800/ 1000]\nloss: 0.000052  [  900/ 1000]\nloss: 0.000111  [    0/ 1000]\nloss: 0.000103  [  100/ 1000]\nloss: 0.000149  [  200/ 1000]\nloss: 0.000132  [  300/ 1000]\nloss: 0.000077  [  400/ 1000]\nloss: 0.000118  [  500/ 1000]\nloss: 0.000074  [  600/ 1000]\nloss: 0.000118  [  700/ 1000]\nloss: 0.000055  [  800/ 1000]\nloss: 0.000085  [  900/ 1000]\nloss: 0.000141  [    0/ 1000]\nloss: 0.000052  [  100/ 1000]\nloss: 0.000064  [  200/ 1000]\nloss: 0.000063  [  300/ 1000]\nloss: 0.000172  [  400/ 1000]\nloss: 0.000172  [  500/ 1000]\nloss: 0.000131  [  600/ 1000]\nloss: 0.000044  [  700/ 1000]\nloss: 0.000104  [  800/ 1000]\nloss: 0.000087  [  900/ 1000]\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 3\n",
    "for epoch in range(1, num_epochs + 1):\n",
    "    train(data_iter, net, loss, optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "OrderedDict([('linear.weight', tensor([[ 2.0004, -3.4002]], device='cuda:0')),\n",
       "             ('linear.bias', tensor([4.2000], device='cuda:0'))])"
      ]
     },
     "metadata": {},
     "execution_count": 97
    }
   ],
   "source": [
    "net.state_dict()"
   ]
  },
  {
   "source": [
    "### Section 5 --- Image identification"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor, Lambda, Compose\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Using cpu device\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Using {} device\".format(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor(),\n",
    ")\n",
    "\n",
    "# Download test data from open datasets.\n",
    "test_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=ToTensor(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([1, 28, 28]) 9\n"
     ]
    }
   ],
   "source": [
    "feature, label = training_data[0]\n",
    "print(feature.shape, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "labels_map = {\n",
    "    0: \"T-Shirt\",\n",
    "    1: \"Trouser\",\n",
    "    2: \"Pullover\",\n",
    "    3: \"Dress\",\n",
    "    4: \"Coat\",\n",
    "    5: \"Sandal\",\n",
    "    6: \"Shirt\",\n",
    "    7: \"Sneaker\",\n",
    "    8: \"Bag\",\n",
    "    9: \"Ankle Boot\",\n",
    "}\n",
    "\n",
    "cols, rows = 8, 2\n",
    "figure = plt.figure(figsize=(cols*2, rows*2))\n",
    "for i in range(1, cols * rows + 1):\n",
    "    sample_idx = torch.randint(len(training_data), size=(1,)).item()\n",
    "    img, label = training_data[sample_idx]\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.title(labels_map[label])\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(img.squeeze())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "\n",
    "train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
    "test_dataloader = DataLoader(test_data, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.linear_relu_stack = nn.Sequential(\n",
    "            nn.Linear(28*28, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 10),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        logits = self.linear_relu_stack(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralNetwork().to(device)\n",
    "\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    size = len(dataloader.dataset)\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        # Compute prediction error\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y)\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if batch % 100 == 0:\n",
    "            loss, current = loss.item(), batch * len(X)\n",
    "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n",
    "\n",
    "def test(dataloader, model, loss_fn):\n",
    "    size = len(dataloader.dataset)\n",
    "    num_batches = len(dataloader)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred = model(X)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
    "    test_loss /= num_batches\n",
    "    correct /= size\n",
    "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1\n",
      "-------------------------------\n",
      "loss: 0.645233  [    0/60000]\n",
      "loss: 0.640402  [ 6400/60000]\n",
      "loss: 0.579991  [12800/60000]\n",
      "loss: 0.448611  [19200/60000]\n",
      "loss: 0.886314  [25600/60000]\n",
      "loss: 0.592785  [32000/60000]\n",
      "loss: 0.540707  [38400/60000]\n",
      "loss: 0.611387  [44800/60000]\n",
      "loss: 0.607069  [51200/60000]\n",
      "loss: 0.493718  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 69.1%, Avg loss: 0.839885 \n",
      "\n",
      "Epoch 2\n",
      "-------------------------------\n",
      "loss: 0.637106  [    0/60000]\n",
      "loss: 0.646540  [ 6400/60000]\n",
      "loss: 0.584787  [12800/60000]\n",
      "loss: 0.445350  [19200/60000]\n",
      "loss: 0.866218  [25600/60000]\n",
      "loss: 0.583533  [32000/60000]\n",
      "loss: 0.536585  [38400/60000]\n",
      "loss: 0.607667  [44800/60000]\n",
      "loss: 0.621634  [51200/60000]\n",
      "loss: 0.475961  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 69.2%, Avg loss: 0.835214 \n",
      "\n",
      "Epoch 3\n",
      "-------------------------------\n",
      "loss: 0.640207  [    0/60000]\n",
      "loss: 0.637039  [ 6400/60000]\n",
      "loss: 0.571082  [12800/60000]\n",
      "loss: 0.443553  [19200/60000]\n",
      "loss: 0.872700  [25600/60000]\n",
      "loss: 0.576747  [32000/60000]\n",
      "loss: 0.523385  [38400/60000]\n",
      "loss: 0.610329  [44800/60000]\n",
      "loss: 0.610717  [51200/60000]\n",
      "loss: 0.489644  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 68.7%, Avg loss: 0.851027 \n",
      "\n",
      "Epoch 4\n",
      "-------------------------------\n",
      "loss: 0.647497  [    0/60000]\n",
      "loss: 0.630392  [ 6400/60000]\n",
      "loss: 0.570106  [12800/60000]\n",
      "loss: 0.428594  [19200/60000]\n",
      "loss: 0.846541  [25600/60000]\n",
      "loss: 0.596258  [32000/60000]\n",
      "loss: 0.548551  [38400/60000]\n",
      "loss: 0.581743  [44800/60000]\n",
      "loss: 0.583628  [51200/60000]\n",
      "loss: 0.532177  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 69.3%, Avg loss: 0.849812 \n",
      "\n",
      "Epoch 5\n",
      "-------------------------------\n",
      "loss: 0.645011  [    0/60000]\n",
      "loss: 0.633026  [ 6400/60000]\n",
      "loss: 0.590199  [12800/60000]\n",
      "loss: 0.439849  [19200/60000]\n",
      "loss: 0.863627  [25600/60000]\n",
      "loss: 0.584487  [32000/60000]\n",
      "loss: 0.540529  [38400/60000]\n",
      "loss: 0.598516  [44800/60000]\n",
      "loss: 0.556512  [51200/60000]\n",
      "loss: 0.490984  [57600/60000]\n",
      "Test Error: \n",
      " Accuracy: 69.1%, Avg loss: 0.865032 \n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "num_epoches = 5\n",
    "\n",
    "for iter in range(num_epoches):\n",
    "    print(f\"Epoch {iter+1}\\n-------------------------------\")\n",
    "    train(train_dataloader, model, loss_fn, optimizer)\n",
    "    test(test_dataloader, model, loss_fn)\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "NeuralNetwork(\n",
       "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
       "  (linear_relu_stack): Sequential(\n",
       "    (0): Linear(in_features=784, out_features=512, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=512, out_features=512, bias=True)\n",
       "    (3): ReLU()\n",
       "    (4): Linear(in_features=512, out_features=10, bias=True)\n",
       "    (5): ReLU()\n",
       "  )\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "source": [
    "### Chapter 5 --- Convolution Neuralnetwork"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "from torch import nn\n",
    "\n",
    "def corr2d(X, K):\n",
    "    h, w = K.shape\n",
    "    Y = torch.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1))\n",
    "    for i in range(Y.shape[0]):\n",
    "        for j in range(Y.shape[1]):\n",
    "            Y[i, j] = (X[i: i + h, j: j + w] * K).sum()\n",
    "    return Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Conv2D(nn.Module):\n",
    "    def __init__(self, kernel_size):\n",
    "        super(Conv2D, self).__init__()\n",
    "        self.weight = nn.Parameter(torch.randn(kernel_size))\n",
    "        self.bias = nn.Parameter(torch.randn(1))\n",
    "\n",
    "    def forward(self, x):\n",
    "        return corr2d(x, self.weight) + self.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "torch.Size([8, 8])"
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "torch.rand(8, 8).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def comp_conv2d(conv2d, X):\n",
    "    X = X.view((1, 1) + X.shape)\n",
    "    Y = conv2d(X)\n",
    "    return Y.view(Y.shape[2:]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "torch.Size([8, 8])"
      ]
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "conv2d = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, padding=1)\n",
    "X = torch.rand(8, 8)\n",
    "\n",
    "comp_conv2d(conv2d, X).shape"
   ]
  },
  {
   "source": [
    "### LeNetWork"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Using cuda device\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "from torch.utils.data import DataLoader\n",
    "import numpy as np\n",
    "\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor, Lambda, Compose\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import sys\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Using {} device\".format(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor(),\n",
    ")\n",
    "\n",
    "# Download test data from open datasets.\n",
    "test_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=ToTensor(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([1, 28, 28]) 9\n"
     ]
    }
   ],
   "source": [
    "feature, label = training_data[0]\n",
    "print(feature.shape, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LeNet, self).__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv2d(1, 6, 5),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),\n",
    "            nn.Conv2d(6, 16, 5),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2)\n",
    "        )\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(16*4*4, 120),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(120, 84),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(84, 10)\n",
    "        )\n",
    "\n",
    "    def forward(self, img):\n",
    "        feature = self.conv(img)\n",
    "        output = self.fc(feature.view(img.shape[0], -1))\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    size = len(dataloader.dataset)\n",
    "\n",
    "    train_l_sum, train_acc_sum, start = 0.0, 0.0, time.time()\n",
    "\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        # Compute prediction error\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y)\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_l_sum += loss.cpu().item()\n",
    "        train_acc_sum += (pred.argmax(dim=1) == y).sum().cpu().item()\n",
    "\n",
    "    print('loss %.4f, train acc %.3f, time %.1f sec'\n",
    "            % (train_l_sum / dataloader.batch_size, train_acc_sum / (size), time.time() - start))\n",
    "\n",
    "    return train_l_sum / dataloader.batch_size, train_acc_sum / (size)\n",
    "\n",
    "\n",
    "def test(dataloader, model, loss_fn):\n",
    "    size = len(dataloader.dataset)\n",
    "    num_batches = len(dataloader)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred = model(X)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            correct += (pred.argmax(1) == y).type(torch.float).sum().cpu().item()\n",
    "    test_loss /= num_batches\n",
    "    correct /= size\n",
    "\n",
    "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n",
    "\n",
    "    return test_loss, correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "\n",
    "train_dataloader = DataLoader(training_data, batch_size=batch_size, shuffle=True)\n",
    "test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LeNet().to(device)\n",
    "\n",
    "loss_fn = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.RMSprop(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epoches = 20\n",
    "\n",
    "train_rec, test_rec = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1\n",
      "-------------------------------\n",
      "loss 0.2051, train acc 0.916, time 3.5 sec\n",
      "Test Error: \n",
      " Accuracy: 88.3%, Avg loss: 0.308643 \n",
      "\n",
      "Epoch 2\n",
      "-------------------------------\n",
      "loss 0.2017, train acc 0.918, time 3.5 sec\n",
      "Test Error: \n",
      " Accuracy: 89.1%, Avg loss: 0.309799 \n",
      "\n",
      "Epoch 3\n",
      "-------------------------------\n",
      "loss 0.1968, train acc 0.920, time 3.6 sec\n",
      "Test Error: \n",
      " Accuracy: 88.9%, Avg loss: 0.316747 \n",
      "\n",
      "Epoch 4\n",
      "-------------------------------\n",
      "loss 0.1917, train acc 0.923, time 3.6 sec\n",
      "Test Error: \n",
      " Accuracy: 88.9%, Avg loss: 0.303688 \n",
      "\n",
      "Epoch 5\n",
      "-------------------------------\n",
      "loss 0.1889, train acc 0.923, time 3.5 sec\n",
      "Test Error: \n",
      " Accuracy: 89.8%, Avg loss: 0.298826 \n",
      "\n",
      "Epoch 6\n",
      "-------------------------------\n",
      "loss 0.1842, train acc 0.925, time 3.4 sec\n",
      "Test Error: \n",
      " Accuracy: 89.1%, Avg loss: 0.321288 \n",
      "\n",
      "Epoch 7\n",
      "-------------------------------\n",
      "loss 0.1801, train acc 0.926, time 3.5 sec\n",
      "Test Error: \n",
      " Accuracy: 89.1%, Avg loss: 0.307386 \n",
      "\n",
      "Epoch 8\n",
      "-------------------------------\n",
      "loss 0.1786, train acc 0.928, time 3.5 sec\n",
      "Test Error: \n",
      " Accuracy: 88.8%, Avg loss: 0.306018 \n",
      "\n",
      "Epoch 9\n",
      "-------------------------------\n",
      "loss 0.1722, train acc 0.929, time 3.5 sec\n",
      "Test Error: \n",
      " Accuracy: 89.1%, Avg loss: 0.312100 \n",
      "\n",
      "Epoch 10\n",
      "-------------------------------\n",
      "loss 0.1695, train acc 0.931, time 3.6 sec\n",
      "Test Error: \n",
      " Accuracy: 88.9%, Avg loss: 0.328511 \n",
      "\n",
      "Epoch 11\n",
      "-------------------------------\n",
      "loss 0.1656, train acc 0.932, time 3.5 sec\n",
      "Test Error: \n",
      " Accuracy: 88.6%, Avg loss: 0.335464 \n",
      "\n",
      "Epoch 12\n",
      "-------------------------------\n",
      "loss 0.1628, train acc 0.933, time 3.3 sec\n",
      "Test Error: \n",
      " Accuracy: 87.7%, Avg loss: 0.357642 \n",
      "\n",
      "Epoch 13\n",
      "-------------------------------\n",
      "loss 0.1577, train acc 0.936, time 3.5 sec\n",
      "Test Error: \n",
      " Accuracy: 89.7%, Avg loss: 0.296523 \n",
      "\n",
      "Epoch 14\n",
      "-------------------------------\n",
      "loss 0.1541, train acc 0.936, time 3.6 sec\n",
      "Test Error: \n",
      " Accuracy: 89.1%, Avg loss: 0.329397 \n",
      "\n",
      "Epoch 15\n",
      "-------------------------------\n",
      "loss 0.1506, train acc 0.939, time 3.6 sec\n",
      "Test Error: \n",
      " Accuracy: 88.9%, Avg loss: 0.334568 \n",
      "\n",
      "Epoch 16\n",
      "-------------------------------\n",
      "loss 0.1492, train acc 0.939, time 3.8 sec\n",
      "Test Error: \n",
      " Accuracy: 89.3%, Avg loss: 0.335925 \n",
      "\n",
      "Epoch 17\n",
      "-------------------------------\n",
      "loss 0.1463, train acc 0.941, time 3.6 sec\n",
      "Test Error: \n",
      " Accuracy: 88.1%, Avg loss: 0.353923 \n",
      "\n",
      "Epoch 18\n",
      "-------------------------------\n",
      "loss 0.1424, train acc 0.942, time 3.6 sec\n",
      "Test Error: \n",
      " Accuracy: 89.7%, Avg loss: 0.356883 \n",
      "\n",
      "Epoch 19\n",
      "-------------------------------\n",
      "loss 0.1403, train acc 0.942, time 3.6 sec\n",
      "Test Error: \n",
      " Accuracy: 89.4%, Avg loss: 0.346020 \n",
      "\n",
      "Epoch 20\n",
      "-------------------------------\n",
      "loss 0.1371, train acc 0.943, time 3.7 sec\n",
      "Test Error: \n",
      " Accuracy: 89.3%, Avg loss: 0.357500 \n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "for iter in range(num_epoches):\n",
    "    print(f\"Epoch {iter+1}\\n-------------------------------\")\n",
    "    train_loss, train_acc = train(train_dataloader, model, loss_fn, optimizer)\n",
    "    test_loss, test_acc = test(test_dataloader, model, loss_fn)\n",
    "\n",
    "    train_rec.append(np.array([train_loss, train_acc]))\n",
    "    test_rec.append(np.array([test_loss, test_acc]))\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([], dtype=float64)"
      ]
     },
     "metadata": {},
     "execution_count": 80
    }
   ],
   "source": [
    "test_rec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0af2282a50>"
      ]
     },
     "metadata": {},
     "execution_count": 83
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "train_record = np.array(train_rec)\n",
    "test_record = np.array(test_rec)\n",
    "\n",
    "plt.plot(train_record[:,0], label='train loss')\n",
    "plt.plot(train_record[:,1], label='train acc')\n",
    "plt.plot(test_record[:,0], label='test loss')\n",
    "plt.plot(test_record[:,1], label='test acc')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1152x288 with 16 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "labels_map = {\n",
    "    0: \"T-Shirt\",\n",
    "    1: \"Trouser\",\n",
    "    2: \"Pullover\",\n",
    "    3: \"Dress\",\n",
    "    4: \"Coat\",\n",
    "    5: \"Sandal\",\n",
    "    6: \"Shirt\",\n",
    "    7: \"Sneaker\",\n",
    "    8: \"Bag\",\n",
    "    9: \"Ankle Boot\",\n",
    "}\n",
    "\n",
    "cols, rows = 8, 2\n",
    "figure = plt.figure(figsize=(cols*2, rows*2))\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for i in range(1, cols * rows + 1):\n",
    "        sample_idx = torch.randint(len(test_data), size=(1,)).item()\n",
    "        img, label = test_data[sample_idx]\n",
    "        pred = model(torch.unsqueeze(img, dim=0)) \n",
    "        predict_label = pred[0].argmax(0).cpu().item()\n",
    "        figure.add_subplot(rows, cols, i)\n",
    "        plt.title(f'{labels_map[label]} -- {labels_map[predict_label]}')\n",
    "        plt.axis(\"off\")\n",
    "        plt.imshow(img.squeeze(), cmap='gray')\n",
    "    plt.show()"
   ]
  },
  {
   "source": [
    "### Alex Network"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Using cuda device\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "from torch.utils.data import DataLoader\n",
    "import numpy as np\n",
    "\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import Resize, ToTensor, Lambda, Compose\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import sys\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Using {} device\".format(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AlexNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(AlexNet, self).__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv2d(1, 96, 11, 4), # in_channels, out_channels, kernel_size, stride, padding\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(3, 2), # kernel_size, stride\n",
    "            # 减小卷积窗口，使用填充为2来使得输入与输出的高和宽一致，且增大输出通道数\n",
    "            nn.Conv2d(96, 256, 5, 1, 2),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(3, 2),\n",
    "            # 连续3个卷积层，且使用更小的卷积窗口。除了最后的卷积层外，进一步增大了输出通道数。\n",
    "            # 前两个卷积层后不使用池化层来减小输入的高和宽\n",
    "            nn.Conv2d(256, 384, 3, 1, 1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(384, 384, 3, 1, 1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(384, 256, 3, 1, 1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(3, 2)\n",
    "        )\n",
    "         # 这里全连接层的输出个数比LeNet中的大数倍。使用丢弃层来缓解过拟合\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(256*5*5, 4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            # 输出层。由于这里使用Fashion-MNIST，所以用类别数为10，而非论文中的1000\n",
    "            nn.Linear(4096, 10),\n",
    "        )\n",
    "\n",
    "    def forward(self, img):\n",
    "        feature = self.conv(img)\n",
    "        output = self.fc(feature.view(img.shape[0], -1))\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    print(\"training on \", device)\n",
    "\n",
    "    size = len(dataloader.dataset)\n",
    "\n",
    "    train_l_sum, train_acc_sum, start = 0.0, 0.0, time.time()\n",
    "\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        # Compute prediction error\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y)\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_l_sum += loss.cpu().item()\n",
    "        train_acc_sum += (pred.argmax(dim=1) == y).sum().cpu().item()\n",
    "\n",
    "    print('loss %.4f, train acc %.3f, time %.1f sec'\n",
    "            % (train_l_sum / dataloader.batch_size, train_acc_sum / (size), time.time() - start))\n",
    "\n",
    "    return train_l_sum / dataloader.batch_size, train_acc_sum / (size)\n",
    "\n",
    "\n",
    "def test(dataloader, model, loss_fn):\n",
    "    size = len(dataloader.dataset)\n",
    "    num_batches = len(dataloader)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred = model(X)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            correct += (pred.argmax(1) == y).type(torch.float).sum().cpu().item()\n",
    "    test_loss /= num_batches\n",
    "    correct /= size\n",
    "\n",
    "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n",
    "\n",
    "    return test_loss, correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/yiwen/.anaconda3/envs/py37/lib/python3.7/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /opt/conda/conda-bld/pytorch_1623448265233/work/torch/csrc/utils/tensor_numpy.cpp:180.)\n  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    }
   ],
   "source": [
    "trans = []\n",
    "trans.append(Resize(size=224))\n",
    "trans.append(ToTensor())\n",
    "\n",
    "transform = Compose(trans)\n",
    "\n",
    "training_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform,\n",
    ")\n",
    "\n",
    "# Download test data from open datasets.\n",
    "test_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "\n",
    "train_dataloader = DataLoader(training_data, batch_size=batch_size, shuffle=True)\n",
    "test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AlexNet().to(device)\n",
    "\n",
    "loss_fn = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epoches = 20\n",
    "\n",
    "train_rec, test_rec = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.6900, train acc 0.708, time 76.8 sec\n",
      "Test Error: \n",
      " Accuracy: 83.5%, Avg loss: 0.442589 \n",
      "\n",
      "Epoch 2\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.3244, train acc 0.868, time 76.5 sec\n",
      "Test Error: \n",
      " Accuracy: 87.8%, Avg loss: 0.333632 \n",
      "\n",
      "Epoch 3\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.2678, train acc 0.890, time 76.7 sec\n",
      "Test Error: \n",
      " Accuracy: 88.2%, Avg loss: 0.320788 \n",
      "\n",
      "Epoch 4\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.2344, train acc 0.904, time 76.4 sec\n",
      "Test Error: \n",
      " Accuracy: 90.1%, Avg loss: 0.266411 \n",
      "\n",
      "Epoch 5\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.2121, train acc 0.913, time 76.9 sec\n",
      "Test Error: \n",
      " Accuracy: 90.2%, Avg loss: 0.267034 \n",
      "\n",
      "Epoch 6\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.1927, train acc 0.921, time 76.7 sec\n",
      "Test Error: \n",
      " Accuracy: 90.8%, Avg loss: 0.266085 \n",
      "\n",
      "Epoch 7\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.1748, train acc 0.928, time 77.0 sec\n",
      "Test Error: \n",
      " Accuracy: 91.3%, Avg loss: 0.248232 \n",
      "\n",
      "Epoch 8\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.1624, train acc 0.934, time 76.2 sec\n",
      "Test Error: \n",
      " Accuracy: 91.1%, Avg loss: 0.263430 \n",
      "\n",
      "Epoch 9\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.1517, train acc 0.939, time 77.0 sec\n",
      "Test Error: \n",
      " Accuracy: 91.6%, Avg loss: 0.244517 \n",
      "\n",
      "Epoch 10\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.1322, train acc 0.946, time 76.5 sec\n",
      "Test Error: \n",
      " Accuracy: 91.5%, Avg loss: 0.255761 \n",
      "\n",
      "Epoch 11\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.1248, train acc 0.949, time 76.3 sec\n",
      "Test Error: \n",
      " Accuracy: 91.6%, Avg loss: 0.254430 \n",
      "\n",
      "Epoch 12\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.1118, train acc 0.955, time 76.4 sec\n",
      "Test Error: \n",
      " Accuracy: 91.5%, Avg loss: 0.298093 \n",
      "\n",
      "Epoch 13\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.1032, train acc 0.957, time 76.0 sec\n",
      "Test Error: \n",
      " Accuracy: 91.8%, Avg loss: 0.268178 \n",
      "\n",
      "Epoch 14\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.0911, train acc 0.963, time 74.1 sec\n",
      "Test Error: \n",
      " Accuracy: 91.6%, Avg loss: 0.284446 \n",
      "\n",
      "Epoch 15\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.0812, train acc 0.967, time 74.3 sec\n",
      "Test Error: \n",
      " Accuracy: 91.3%, Avg loss: 0.298499 \n",
      "\n",
      "Epoch 16\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.0727, train acc 0.970, time 74.4 sec\n",
      "Test Error: \n",
      " Accuracy: 91.4%, Avg loss: 0.350615 \n",
      "\n",
      "Epoch 17\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.0701, train acc 0.972, time 76.7 sec\n",
      "Test Error: \n",
      " Accuracy: 92.2%, Avg loss: 0.297868 \n",
      "\n",
      "Epoch 18\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.0594, train acc 0.976, time 73.8 sec\n",
      "Test Error: \n",
      " Accuracy: 92.0%, Avg loss: 0.372438 \n",
      "\n",
      "Epoch 19\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.0560, train acc 0.977, time 76.8 sec\n",
      "Test Error: \n",
      " Accuracy: 91.7%, Avg loss: 0.347687 \n",
      "\n",
      "Epoch 20\n",
      "-------------------------------\n",
      "training on  cuda\n",
      "loss 0.0563, train acc 0.978, time 76.7 sec\n",
      "Test Error: \n",
      " Accuracy: 91.6%, Avg loss: 0.366596 \n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "for iter in range(num_epoches):\n",
    "    print(f\"Epoch {iter+1}\\n-------------------------------\")\n",
    "    train_loss, train_acc = train(train_dataloader, model, loss_fn, optimizer)\n",
    "    test_loss, test_acc = test(test_dataloader, model, loss_fn)\n",
    "\n",
    "    train_rec.append(np.array([train_loss, train_acc]))\n",
    "    test_rec.append(np.array([test_loss, test_acc]))\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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     "metadata": {},
     "execution_count": 15
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     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "train_record = np.array(train_rec)\n",
    "test_record = np.array(test_rec)\n",
    "\n",
    "plt.plot(train_record[:,0], label='train loss')\n",
    "plt.plot(train_record[:,1], label='train acc')\n",
    "plt.plot(test_record[:,0], label='test loss')\n",
    "plt.plot(test_record[:,1], label='test acc')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "RuntimeError",
     "evalue": "Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-7d94c26a05b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0msample_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msample_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m         \u001b[0mpredict_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-2-33d0145c4e44>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0mfeature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    438\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[1;32m    439\u001b[0m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[0;32m--> 440\u001b[0;31m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor"
     ]
    }
   ],
   "source": [
    "labels_map = {\n",
    "    0: \"T-Shirt\",\n",
    "    1: \"Trouser\",\n",
    "    2: \"Pullover\",\n",
    "    3: \"Dress\",\n",
    "    4: \"Coat\",\n",
    "    5: \"Sandal\",\n",
    "    6: \"Shirt\",\n",
    "    7: \"Sneaker\",\n",
    "    8: \"Bag\",\n",
    "    9: \"Ankle Boot\",\n",
    "}\n",
    "\n",
    "cols, rows = 8, 2\n",
    "figure = plt.figure(figsize=(cols*2, rows*2))\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for i in range(1, cols * rows + 1):\n",
    "        sample_idx = torch.randint(len(test_data), size=(1,)).item()\n",
    "        img, label = test_data[sample_idx]\n",
    "        pred = model(torch.unsqueeze(img, dim=0)) \n",
    "        predict_label = pred[0].argmax(0).cpu().item()\n",
    "        figure.add_subplot(rows, cols, i)\n",
    "        plt.title(f'{labels_map[label]} -- {labels_map[predict_label]}')\n",
    "        plt.axis(\"off\")\n",
    "        plt.imshow(img.squeeze(), cmap='gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}